1 City



PHOTO BY TYLER MERBLER
Los Angeles, CA has a population of 3.98M people with a median age of 35.6 and a median household income of $54,432. # County

PHOTO BY PAOLO GAMBA
Los Angeles County, CA has a population of 10.1M people with a median age of 36.3 and a median household income of $61,338.
The tech industry ….. doing well….. Many of the most valuable companies are tehc companies
Let’s take a look at the tech industry employment [1] numbers for the past 13 years.

2 Employment Projections

2017 Employment by major occupational group, 2016 and projected 2026 (Numbers in thousands) [1]. Tech employees are compensated nicely due to this tech boom. For example, in 207 while the annual medialn wage for all occupations was $37,000 in the U.S., it was $84,600 for the computer occupations, second only to $102,000 of the management occupations as shown in Table 1.

3 Dinamic Visualizations


Total jobs in the city of Los Angeles and County

4 Maps

2017 Dot density map for each zip code LA county.
Each dot = 10 tech jobs


5 Correlation Between the CHCI and the Median House Income of Los Angeles County Zip Codes


Fig. 8 shows the association between the 2012 CHCI and the 2016 median household income of 282 zip codes in L.A. County. We can see a very strong correlation correlation between the human capital level [23] and the household income for the zip code in L.A. It demonstrates the importance to invest in education (chci) in order to create more productive workforce and more high paying jobs.

##       zip             year           chci             pop       
##  Min.   :90001   Min.   :2012   Min.   : 83.89   Min.   :   77  
##  1st Qu.:90230   1st Qu.:2012   1st Qu.:125.44   1st Qu.:15292  
##  Median :90746   Median :2012   Median :140.94   Median :22144  
##  Mean   :90908   Mean   :2012   Mean   :142.21   Mean   :23366  
##  3rd Qu.:91364   3rd Qu.:2012   3rd Qu.:161.54   3rd Qu.:29712  
##  Max.   :93591   Max.   :2012   Max.   :191.67   Max.   :66710  
##                                 NA's   :3        NA's   :3      
##      NAME             variable            estimate           moe       
##  Length:285         Length:285         Min.   : 12370   Min.   :  694  
##  Class :character   Class :character   1st Qu.: 46323   1st Qu.: 2575  
##  Mode  :character   Mode  :character   Median : 62349   Median : 3960  
##                                        Mean   : 67387   Mean   : 5599  
##                                        3rd Qu.: 81440   3rd Qu.: 6272  
##                                        Max.   :178194   Max.   :81747  
##                                        NA's   :5        NA's   :5

## 'data.frame':    311 obs. of  8 variables:
##  $ GEOID: num  90001 90002 90003 90004 90005 ...
##  $ V1   : num  5545 5546 5547 5548 5549 ...
##  $ year : num  2017 2017 2017 2017 2017 ...
##  $ tech : num  96 5 33 784 606 ...
##  $ info : num  19 0 0 224 279 77 71 111 860 0 ...
##  $ prof : num  77 5 33 560 327 ...
##  $ per  : num  1.03 0.15 0.49 7.61 8.88 ...
##  $ total: num  9334 3320 6720 10296 6824 ...
## 'data.frame':    315 obs. of  9 variables:
##  $ GEOID: chr  "90001" "90002" "90003" "90004" ...
##  $ V1   : num  5545 5546 5547 5548 5549 ...
##  $ year : num  2017 2017 2017 2017 2017 ...
##  $ tech : num  96 5 33 784 606 ...
##  $ info : num  19 0 0 224 279 77 71 111 860 0 ...
##  $ prof : num  77 5 33 560 327 ...
##  $ per  : num  1.03 0.15 0.49 7.61 8.88 ...
##  $ total: num  9334 3320 6720 10296 6824 ...
##  $ name : chr  "Florence/South Central (City of LA)" "Watts (City of LA)" "South Central (City of LA)" "Hancock Park (City of LA)" ...
## 'data.frame':    285 obs. of  8 variables:
##  $ zip     : num  90001 90002 90003 90004 90005 ...
##  $ year    : num  2012 2012 2012 2012 2012 ...
##  $ chci    : num  94.4 100.2 100.1 135.5 125.4 ...
##  $ pop     : num  30443 26162 35195 44115 26814 ...
##  $ NAME    : chr  "ZCTA5 90001" "ZCTA5 90002" "ZCTA5 90003" "ZCTA5 90004" ...
##  $ variable: chr  "medincome" "medincome" "medincome" "medincome" ...
##  $ estimate: num  34323 32520 31878 43180 31485 ...
##  $ moe     : num  1369 2461 1140 2040 862 ...
## 'data.frame':    20 obs. of  9 variables:
##  $ zip  : chr  "91608" "91504" "91521" "90094" ...
##  $ V1   : num  5938 5923 5928 5632 5699 ...
##  $ year : num  2017 2017 2017 2017 2017 ...
##  $ tech : num  14157 67206 5929 3300 13764 ...
##  $ info : num  14012 66341 5009 1136 8751 ...
##  $ prof : num  145 865 920 2164 5013 ...
##  $ per  : num  83.2 82.1 72.5 57.8 44 ...
##  $ total: num  17021 81882 8179 5705 31296 ...
##  $ name : chr  "Universal City" "Burbank (Glenoaks)" "Burbank" "Playa Vista" ...

##       zip              V1           year.x          tech      
##  Min.   :90025   Min.   :5569   Min.   :2017   Min.   : 3300  
##  1st Qu.:90046   1st Qu.:5590   1st Qu.:2017   1st Qu.: 5163  
##  Median :90081   Median :5622   Median :2017   Median : 8362  
##  Mean   :90380   Mean   :5675   Mean   :2017   Mean   :12150  
##  3rd Qu.:90319   3rd Qu.:5683   3rd Qu.:2017   3rd Qu.:12653  
##  Max.   :91505   Max.   :5924   Max.   :2017   Max.   :67206  
##       info            prof            per            total      
##  Min.   : 1136   Min.   :  387   Min.   :23.94   Min.   : 5705  
##  1st Qu.: 2960   1st Qu.: 1495   1st Qu.:27.64   1st Qu.:15770  
##  Median : 4240   Median : 3533   Median :38.02   Median :24458  
##  Mean   : 8079   Mean   : 4071   Mean   :38.18   Mean   :28861  
##  3rd Qu.: 5713   3rd Qu.: 5174   3rd Qu.:41.28   3rd Qu.:33567  
##  Max.   :66341   Max.   :11426   Max.   :82.08   Max.   :81882  
##      name               year.y          chci            pop       
##  Length:16          Min.   :2012   Min.   :132.0   Min.   :   77  
##  Class :character   1st Qu.:2012   1st Qu.:158.0   1st Qu.:11548  
##  Mode  :character   Median :2012   Median :163.1   Median :19304  
##                     Mean   :2012   Mean   :163.9   Mean   :19052  
##                     3rd Qu.:2012   3rd Qu.:172.3   3rd Qu.:22761  
##                     Max.   :2012   Max.   :190.3   Max.   :43142  
##      NAME             variable            estimate           moe       
##  Length:16          Length:16          Min.   : 35729   Min.   : 2465  
##  Class :character   Class :character   1st Qu.: 72945   1st Qu.: 3947  
##  Mode  :character   Mode  :character   Median : 80306   Median : 5436  
##                                        Mean   : 85072   Mean   : 7942  
##                                        3rd Qu.: 98721   3rd Qu.: 7690  
##                                        Max.   :137683   Max.   :30762

Source - 5-year American Community Survey 2012-2016 Global Innovation Index 2018, World Bank Population Data 2016, Q11 - Current country of residence [19-21]

zip V1 year.x tech info prof per total name year.y chci pop NAME variable estimate moe
91504 5923 2017 67206 66341 865 82.08 81882 Burbank (Glenoaks) 2012 148.76 18328 ZCTA5 91504 medincome 74006 6132
90094 5632 2017 3300 1136 2164 57.84 5705 Playa Vista 2012 163.67 77 ZCTA5 90094 medincome 121087 19999
90404 5699 2017 13764 8751 5013 43.98 31296 Santa Monica 2012 162.47 16235 ZCTA5 90404 medincome 73042 4547
91505 5924 2017 15032 7067 7965 43.74 34365 Burbank 2012 152.61 22602 ZCTA5 91505 medincome 80389 3788
90067 5611 2017 12283 2740 9543 40.46 30360 Century City (City of LA) 2012 169.58 2126 ZCTA5 90067 medincome 120208 30762
90064 5608 2017 10638 6178 4460 39.41 26993 Cheviot Hills (City of LA)/Rancho Park (City of LA) 2012 176.98 18808 ZCTA5 90064 medincome 92626 5326

6 Conclusions

7 References

  1. [1] Assumptions Tech industry employment was calculated using the following: (1) Information jobs (NAICS: 51), and (2) Professional, Scientific, & Technical Skills (NAICS: 54). Data source: Quarterly Census of Employment and Wages (QCEW) developed through a cooperative program between the states and the U.S. Bureau of Labor Statistics.– These data are summarized by Industry Sector (2-digit NAICS).
  2. [2] https://www.bls.gov/emp/tables/emp-by-major-occupational-group.htm

Shape Files - https://data.lacity.org/ Los Angeles Open Data ACS

  1. [23] http://www.anderson.ucla.edu/centers/ucla-anderson-forecast/projects-and-partnerships/city-human-capital-index

8 Acknowledgements

https://flowingdata.com/

https://geocompr.robinlovelace.net/index.html

#googleWalkout [2] and is bad business - says Forbes [3]. In Fig. 1 (above), we use superhero-themes #batman #wonderwoman to visualize the heavy topic of #gender_equality in #datascience. See a bar chart for a more accurate breakdown [4]. Source: survey question Q1 - What is your gender? Sample size = 23,859 respondents

  1. [2] https://www.theverge.com/2018/11/2/18057716/google-walkout-20-thousand-employees-ceo-sundar-pichai-meeting
  2. [3] https://www.forbes.com/sites/womensmedia/2017/08/03/breaking-down-the-gender-gap-in-data-science/#129d1bb74287
  3. [4] https://www.kaggle.com/paultimothymooney/2018-kaggle-machine-learning-data-science-survey
  4. [5] https://en.wikipedia.org/wiki/Generations_in_the_workforce
  5. [6] Sinton, E (2011). ‘Baby boomers are very privileged human beings’ https://www.telegraph.co.uk/finance/personalfinance/pensions/8840963/Baby-boomers-are-very-privileged-human-beings.html retrieved October 23, 2013 from www.telegraph.co.uk
  6. [7] Ken Blanchard Companies. (2009). Next Generation of workers. http://www.kenblanchard.com/img/pub/Blanchard_Next_Generation_of_Workers.pdf Retrieved October 14, 2013, from kenblanchard.com
  7. [8] Adecco Group UK and Ireland. (n.d.). Managing the modern workforce. http://www.adeccogroupuk.co.uk/SiteCollectionDocuments/Adecco-Group-Workplace-Revolution.pdf Retrieved October 13, 2013, from www.Adeccouk.co.uk
  8. ref. needed
  9. [10] https://en.wikipedia.org/wiki/Affluence_in_the_United_States
  10. [11] https://www.epi.org/blog/top-1-0-percent-reaches-highest-wages-ever-up-157-percent-since-1979/
  11. [12] J. Berengueres, Sketch thinking. 2016
  12. [13] https://en.wikipedia.org/wiki/Marimekko#Marimekko_chart
  13. [14] ref. needed
  14. [15] https://www.kaggle.com/ash316/kaggle-journey-2017-2018
  15. [16] https://en.wikipedia.org/wiki/BRICS
  16. [17] https://www.kaggle.com/harriken/brics-growth
  17. [18] See primary vs. secondary color in https://material.io/design/color/the-color-system.html#color-theme-creation
  18. [19] Dutta, S., Reynoso, R.E., Garanasvili, A., Saxena, K., Lanvin, B., Wunsch-Vincent, S., Le?n, L.R. and Guadagno, F., 2018. THE GLOBAL INNOVATION INDEX 2018: ENERGIZING THE WORLD WITH INNOVATION. GLOBAL INNOVATION INDEX 2018, p.1.
  19. [20] CSV file global innovation in https://www.globalinnovationindex.org/analysis-indicator
  20. [21] World Bank, https://data.worldbank.org/indicator/SP.POP.TOTL
  21. [22] https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient

The shapefile format is a common way to store data for geographic areas, namely polygons, lines, and points. Each tract is represented as a polygon (i.e. some shape)

Dowload shape files for county from LA Data Portal https://data.lacounty.gov/Geospatial/ZIP-Codes/65v5-jw9f